{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "from os.path import dirname, realpath, join\n",
    "base_dir = dirname(dirname(os.getcwd()))\n",
    "base_dir\n",
    "import pandas as pd\n",
    "from os.path import join\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(0, base_dir)\n",
    "from config_path import PROSTATE_DATA_PATH, PLOTS_PATH, PROSTATE_LOG_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(ax, y_test, y_pred_score, save_dir,color, label=''):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_score, pos_label=1)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    # plt.figure(fig.number)\n",
    "#     plt.plot(fpr, tpr, label=label + ' (area = %0.2f)' % roc_auc, linewidth=2, color=color)\n",
    "#     plt.plot(fpr, tpr, label=label + ' (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "    ax.plot(fpr, tpr, label=label + ' (area = %0.2f)' % roc_auc, linewidth=2, color=color)\n",
    "    # plt.plot(fpr, tpr)\n",
    "    ax.plot([0, 1], [0, 1], 'k--', alpha=0.1)\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_ylim([0.0, 1.05])\n",
    "\n",
    "    ax.set_xlabel('False Positive Rate', fontproperties)\n",
    "    ax.set_ylabel('True Positive Rate', fontproperties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pnet_base_dir = join(PROSTATE_LOG_PATH , 'pnet/onsplit_average_reg_10_tanh_large_testing')\n",
    "df_pnet = pd.read_csv(join(pnet_base_dir, 'P-net_ALL_testing.csv'), sep=',', index_col=0, header=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_base_dir = join(PROSTATE_LOG_PATH , 'dense/onsplit_dense')\n",
    "df_dense = pd.read_csv(join(models_base_dir, 'P-net_ALL_testing.csv'), sep=',', index_col=0, header=[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>pred_scores</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-087MM_BONE</th>\n",
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       "      <th>08-093J1_LN</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10362</th>\n",
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       "      <td>0.225192</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>AAPC-IP_LG-069-Tumor-SM-3NC72</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               pred  pred_scores  y\n",
       "01-087MM_BONE                   1.0     0.457993  1\n",
       "01-095N1_LN                     0.0     0.214757  1\n",
       "08-093J1_LN                     1.0     0.500848  1\n",
       "10362                           0.0     0.225192  0\n",
       "AAPC-IP_LG-069-Tumor-SM-3NC72   0.0     0.207921  0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dense.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>pred_scores</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-087MM_BONE</th>\n",
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       "      <th>01-095N1_LN</th>\n",
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       "      <th>08-093J1_LN</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10362</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.475796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               pred  pred_scores  y\n",
       "01-087MM_BONE                   1.0     0.946647  1\n",
       "01-095N1_LN                     0.0     0.127979  1\n",
       "08-093J1_LN                     1.0     0.990789  1\n",
       "10362                           1.0     0.475796  0\n",
       "AAPC-IP_LG-069-Tumor-SM-3NC72   0.0     0.114404  0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pnet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, sharey=True, figsize=(5,4), dpi=200)\n",
    "fontproperties = {'family': 'Arial', 'weight': 'bold', 'size': 12}\n",
    "y_test = df_pnet['y']\n",
    "y_pred_score = df_pnet['pred_scores']\n",
    "colors = sns.color_palette(None, 2)\n",
    "plot_roc(ax, y_test, y_pred_score, None, color=colors[0], label='P-NET (70K params)')\n",
    "\n",
    "y_test = df_dense['y']\n",
    "y_pred_score = df_dense['pred_scores']\n",
    "plot_roc(ax, y_test, y_pred_score, None, color=colors[1], label='Dense (14M params)')\n",
    "plt.legend(loc=\"lower right\", fontsize=8, framealpha=0.0)\n",
    "\n",
    "plt.savefig('dense_same_arch')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:min_env]",
   "language": "python",
   "name": "conda-env-min_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
